Assessing Single-Objective Performance Convergence and Time Complexity for Refactoring Detection

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  author =       "David Nader-Palacio and Daniel Rodriguez-Cardenas and 
                 Jonatan Gomez",
  title =        "Assessing Single-Objective Performance Convergence and
                 Time Complexity for Refactoring Detection",
  booktitle =    "5th edition of GI @ GECCO 2018",
  year =         "2018",
  editor =       "Brad Alexander and Saemundur O. Haraldsson and 
                 Markus Wagner and John R. Woodward and Shin Yoo",
  pages =        "1606--1613",
  address =      "Kyoto, Japan",
  month =        "15-19 " # jul,
  organisation = "ACM SIGEvo",
  publisher =    "ACM",
  keywords =     "genetic algorithms, genetic programming, genetic
                 improvement, SBSE, Combinatorial Optimization,
                 Mathematical Software Performance, Refactoring,
                 Software Maintenance",
  isbn13 =       "978-1-4503-5764-7",
  acmid =        "3208294",
  URL =          "",
  DOI =          "doi:10.1145/3205651.3208294",
  size =         "8 pages",
  abstract =     "The automatic detection of refactoring recommendations
                 has been tackled in prior optimization studies
                 involving bad code smells, semantic coherence and
                 importance of classes; however, such studies informally
                 addressed formalisms to standardize and replicate
                 refactoring models. We propose to assess the
                 refactoring detection by means of performance
                 convergence and time complexity. Since the reported
                 approaches are difficult to reproduce, we employ an
                 Artificial Refactoring Generation (ARGen) as a formal
                 and naive computational solution for the Refactoring
                 Detection Problem. ARGen is able to detect massive
                 refactoring sets in feasible areas of the search space.
                 We used a refactoring formalization to adapt search
                 techniques (Hill Climbing, Simulated Annealing and
                 Hybrid Adaptive Evolutionary Algorithm) that assess the
                 performance and complexity on three open software
                 systems. Combinatorial techniques are limited in
                 solving the Refactoring Detection Problem due to the
                 relevance of developers' criteria (human factor) when
                 designing reconstructions. Without performance
                 convergence and time complexity analysis, a software
                 empirical analysis that uses search techniques is
  notes =        "HaEa p1610 'Commons Codec v.1.10 with 123 number of
                 classes (CCODEC), Acra v.4.6.0 with 59 classes (ACRA)
                 and JFreeChart v.1.0.9. with 558


Genetic Programming entries for David Alberto Nader-Palacio Daniel Rodriguez-Cardenas Jonatan Gomez Perdomo